Data Analysis: A Bayesian Tutorial 2nd Revised edition

Sivia (St. Catherine's College) and Skilling, a data consultant, offer a unified approach to the study of data analysis. The text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. After explaining the basic principles of Bayesian probability theory, the book illustrates their use with a range of examples. Topics covered include reliability analysis, multivariate optimization, least-squares and maximum likelihood, and error-propagation. This second edition contains a new chapter on extensions to the ubiquitous least-squares procedure, and new material on a novel numerical technique for Bayesian computation called 'nested sampling.' Worked examples are included. Annotation ©2006 Book News, Inc., Portland, OR (booknews.com)
Statistics lectures have been a source of much bewilderment and frustration for generations of students. This book attempts to remedy the situation by expounding a logical and unified approach to the whole subject of data analysis.This text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. Other topics covered include reliability analysis, multivariate optimization, least-squares and maximum likelihood, error-propagation, hypothesis testing, maximum entropy and experimental design.The Second Edition of this successful tutorial book contains a new chapter on extensions to the ubiquitous least-squares procedure, allowing for the straightforward handling of outliers and unknown correlated noise, and a cutting-edge contribution from John Skilling on a novel numerical technique for Bayesian computation called 'nested sampling'.